Video imaging is a quickly evolving field that involves the use of image capture technology, such as a camera, to capture and/or store images. Its usefulness includes the ability to capture images without having a human present and/or to store details of an event and/or object that no human could remember and/or accurately convey to others. To this extent, video imaging is often used in places where it is difficult or impossible to go (e.g., inside the human body, outer space, etc.) and/or in places where there is a shortage of humans needed for observation (e.g., security).
In the past, imaging technology has been limited to simply capturing and/or storing information. However, these systems may require users to manually traverse large amounts of data to obtain results from the specific event and/or object that they are interested in. These systems may be thought of as reactive because analysis can only be performed after the system has captured and stored the image.
In contrast, present day users desire “smart systems” for providing automatic analysis of the information that they capture. For example, in the area of security, users may desire that the system be able to analyze the data and provide the user with specific events, such as those that differ from the norm. Furthermore, users may desire these systems to be proactive, that is, to analyze information as it is being received and to take some action if the data indicates that a certain event may be occurring.
One difficulty in providing “smart systems” that have the functionality that users desire has been the difficulty in resolving occlusions. An occlusion is an event in which, from the perspective of the video imaging instrument, two or more objects occupy the same area of the captured frame. For example, if two cars are driving in the same direction and one car passes the other, the car that is “in front” with respect to the perspective of the video imaging instrument will occlude the cars that is “behind.” From the perspective of the video imaging instrument it will appear that one car has partially or fully disappeared or that the two cars have merged together to form one single object. Another example involves a situation in which two or more objects enter the “view” of the video imaging instrument while an occlusion is occurring. In this example, the system may have difficulty determining whether the occlusion contains one object or more than one object. Furthermore, if the occlusion resolves itself while in the “view” of the video imaging device, it may be difficult for the system to determine whether the now multiple objects or single object should be classified as a single object or multiple objects.
In view of the foregoing, there exists a need for a solution that overcomes one or more of the shortcomings of the prior art.